Behavior and intent estimations of road users for autonomous vehicles

ABSTRACT

As an example, data identifying characteristics of a road user as well as contextual information about the vehicle&#39;s environment is received from the vehicle&#39;s perception system. A prediction of the intent of the object including an action of a predetermined list of actions to be initiated by the road user and a point in time for initiation of the action is generated using the data. A prediction of the behavior of the road user for a predetermined period of time into the future indicating that the road user is not going to initiate the action during the predetermined period of time is generated using the data. When the prediction of the behavior indicates that the road user is not going to initiate the action during the predetermined period of time, the vehicle is maneuvered according to the prediction of the intent prior to the vehicle passing the object.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/239,367, filed Aug. 17, 2016, the disclosure of which is incorporatedherein by reference.

BACKGROUND

Autonomous vehicles, such as vehicles that do not require a humandriver, can be used to aid in the transport of passengers or items fromone location to another. Such vehicles may operate in a fully autonomousmode where passengers may provide some initial input, such as a pickupor destination location, and the vehicle maneuvers itself to thatlocation.

Such vehicles are typically equipped with various types of sensors inorder to detect objects in the surroundings. For example, autonomousvehicles may include lasers, sonar, radar, cameras, and other deviceswhich scan and record data from the vehicle's surroundings. Sensor datafrom one or more of these devices may be used to detect objects andtheir respective characteristics (position, shape, heading, speed,etc.). These characteristics can be used to predict what an object islikely to do for some brief period into the future which can be used tocontrol the vehicle in order to avoid these objects. Thus, detection,identification, and prediction are critical functions for the safeoperation of autonomous vehicle.

BRIEF SUMMARY

One aspect of the disclosure provides a method of maneuvering a vehicle.The method includes receiving, by one or more processors, data from aperception system of the vehicle identifying an object corresponding toa road user, the information also identifying characteristics of theroad user as well as contextual information about an environment inwhich the vehicle is currently driving; generating, by the one or moreprocessors, a prediction of the intent of the object using the data,wherein the prediction of the intent of the object includes a nextaction to be initiated by the road user and a point in time forinitiation of the next action, wherein the next action is included in apredetermined list of actions; generating, by the one or moreprocessors, a prediction of the behavior of the road user for apredetermined period of time into the future using the data, theprediction of the behavior indicating a likelihood that the road userwill initiate the next action during the predetermined period of time,the predetermined period of time including at least a period of timeduring which the vehicle is expected to have passed the object; and whenthe prediction of the behavior indicates that the road user is notlikely to initiate the next action during the predetermined period oftime, maneuvering, by the one or more processors, the vehicle accordingto the prediction of the intent prior to the vehicle passing the object.

In one example, the prediction of the intent includes the road usercrossing the roadway in a crosswalk. In another example, the predictionof the intent includes the road user crossing a lane of a roadway not ina crosswalk. In another example, the method also includes generating aset of possible intents of the object using the data, wherein the eachpossible intent of the set of intents includes a corresponding nextaction to be initiated by the road user, wherein the prediction of theintent is a most likely possible intent of the set of possible intents.In this example, the method also includes, when the prediction of thebehavior indicates that the road user is not likely to initiate a nextaction of a second possible intent of the set of intents during thepredetermined period of time, maneuvering, by the one or moreprocessors, the vehicle according to both the prediction of the intentand the second possible intent prior to the vehicle passing the object.In another example, the prediction of the intent includes the road userentering a driveway from the roadway. In another example, the predictionof the intent includes at least one of the road user entering or exitinga driveway onto the roadway. In another example, the prediction of theintent includes the road user unparking and entering the roadway. Inanother example, wherein the prediction of intent includes the road userpassing through an intersection. In another example, the prediction ofintent includes the road user changing lanes.

Another aspect of the disclosure provides a system for maneuvering avehicle. The system includes one or more processors configured toreceive data from a perception system of the vehicle identifying anobject corresponding to a road user, the information also identifyingcharacteristics of the road user as well as contextual information aboutan environment in which the vehicle is currently driving; generate aprediction of the intent of the object using the data, wherein theprediction of the intent of the object includes a next action to beinitiated by the road user and a point in time for initiation of thenext action, wherein the next action is included in a predetermined listof actions; generate a prediction of the behavior of the road user for apredetermined period of time into the future using the data, theprediction of the behavior indicating that the road user is not going toinitiate the next action during the predetermined period of time, thepredetermined period of time including at least a period of time duringwhich the vehicle is expected to have passed the object; and when theprediction of the behavior indicates that the road user is not going toinitiate the next action during the predetermined period of time,maneuver the vehicle according to the prediction of the intent prior tothe vehicle passing the object.

In one example, the prediction of the intent includes the road usercrossing the roadway in a crosswalk. In another example, the predictionof the intent includes the road user crossing a lane of a roadway not ina crosswalk. In another example, the one or more processors are furtherconfigured to generate a set of possible intents of the object using thedata, wherein the each possible intent of the set includes acorresponding next action to be initiated by the road user, wherein theprediction of the intent is a most likely possible intent of the set ofpossible intents. In this example, the one or more processors arefurther configured to, when the prediction of the behavior indicatesthat the road user is not likely to initiate a next action of a secondpossible intent of the set of intents during the predetermined period oftime, maneuver the vehicle according to both the prediction of theintent and the second possible intent prior to the vehicle passing theobject.

A further aspect of the disclosure provides a method of maneuvering avehicle. The method includes receiving information from a perceptionsystem of the vehicle identifying an object corresponding to a roaduser, the information also identifying characteristics of the object aswell as contextual information about an environment in which the vehicleis currently driving; generating a prediction of the behavior of theobject for a predetermined period of time into the future using theinformation, the prediction indicating that the object is not going tobegin to cross a roadway during the predetermined period of time, thepredetermined period of time including at least a period of time duringwhich the vehicle will have passed the object if the vehicle continuesat a current speed, acceleration, and heading; generating a predictionof the intent of the object using the information, wherein the intent ofthe object includes intending to begin to cross the roadway at a pointin time that is after an end of the predetermined period of time intothe future; and maneuvering the vehicle according to the prediction ofthe intent by allowing the object to cross the roadway prior to thevehicle passing the object.

In one example, generating the prediction of the intent includesgenerating a set of predictions, wherein the set of predictions includesthe prediction of the intent, and each given prediction of the set ofpredictions identifies a next possible action by the road user. In thisexample, beginning to cross the roadway is the next possible action bythe road user for the prediction of intent. In another example, theprediction of the intent includes the road user crossing the roadway ina crosswalk. In another example, the road user is a pedestrian.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional diagram of an example vehicle in accordance withan exemplary embodiment.

FIG. 2 is a functional diagram of an example system in accordance withan exemplary embodiment.

FIGS. 3A-3D are a pictorial diagram of the system of FIG. 2 inaccordance with aspects of the disclosure.

FIG. 4 is an example external views of a vehicle in accordance withaspects of the disclosure.

FIG. 5 is an example internal view of a vehicle in accordance withaspects of the disclosure.

FIGS. 6-9 are example views of vehicles in accordance with aspects ofthe disclosure.

FIG. 10 is an example flow diagram in accordance with aspects of thedisclosure.

DETAILED DESCRIPTION Overview

Aspects of the technology relate to controlling autonomous vehicles,including for instances various cars, sport utility vehicles, busses,trucks, tractor-trailers, motorcycles, etc. Generally, these vehiclesoperate by detecting and identifying objects in the vehicle'senvironment. More sophisticated systems actually make predictions aboutwhat those objects (especially other road users like vehicles,bicyclists, and pedestrians) are likely to do for at least some shortperiod of time into the future. Predicting what other objects are goingto do is important so that the autonomous vehicle can act pre-emptivelyand make sure that the vehicle will not interfere with other road usersor compromise their safety. However, there is a distinction between whatan object will do for some period of time versus predicting what thatobject would like to do or its intent. For instance, a pedestrianstanding still on the side of the road may want to cross but they won'tstep out in front of a vehicle unless that vehicle slows down and allowsfor them to cross. In a prediction sense, predicting that such apedestrian will stay on the sidewalk for the next 5 seconds may bestrictly correct. However, such predictions alone fail to consider whatthat pedestrian wants to do or rather, his or her intent. In otherwords, the pedestrian may really want to cross the road and will startto cross as soon as there is a gap in the stream of cars or some carstops for them.

In order to both make a prediction and reason about an object's intent,the vehicle's computing devices may receive information from a highlysophisticated perception system. For instance, a vehicle's perceptionsystem may use various sensors, such as LIDAR, sonar, radar, cameras,etc. to detect objects and their characteristics such as location,orientation, size, shape, type, direction and speed of movement, etc.These characteristics can be quantified or arranged into a descriptivefunction or vector.

Data received from the perception system may be used by a predictionsystem to make a prediction about what objects are going to do for apredetermined period of time. This is typically done by reasoning overwhat another road user will do over some time horizon. For instance, thevehicle's computing devices may generate a prediction of what apedestrian will do over the next ten seconds, or over a time periodcorresponding to how long the autonomous vehicle, may interact with thepedestrian (e.g. until the pedestrian is well behind the autonomousvehicle).

In one example, data received from the perception system for an object,and in particular another road user, including the road user'scharacteristics as well as additional contextual information may be fedinto a behavior-time model. The model may generate a set of possiblehypotheses for a particular road user's behavior or what a particularroad user will do in the future. The behavior-time models may providethese hypotheses for a particular time horizon or predetermined periodof time and relative likelihoods for each hypothesis. Thesebehavior-time models may be trained using data about how an objectobserved at that location behaved in the past, intuition, etc. Theautonomous vehicle's computing devices can then reason about hypothesesthat interact with the vehicle's future trajectory (for instance,defining a set of future locations where the vehicle will be at varioustimes in the future) and are of a sufficient likelihood to be worthconsidering.

In addition, the vehicle's computing devices may generate a set ofpossible intents for a given road user by looking at all the possibleactions that the road user could execute using intent models, similarlyto generating actions for the predictions described above. However, incontrast to the predictions, these intents are not based on a fixed timehorizon or predetermined period of time, but rather reflect the nextaction the road user will take. So for the case of a pedestrian standingon the side of the road waiting to cross, the pedestrian may beintending to cross and this is reflected by the fact that crossing theroad is the next action the pedestrian takes. However, this action maynot occur until the road is clear, which may be an arbitrary amount oftime in the future.

A next action may be defined as an action that the road user will beginto take after waiting for some period of time or simply the next actionthe road user is expected to take which falls into a predetermined,pre-stored list of actions. Thus, to train a model to generatelikelihoods for these intents, rather than using a fixed time horizonand observing what the object does during that fixed time horizon, thetraining data may look as far into the future as it takes for the roaduser to perform any action included in the list of predeterminedactions. Then, the next action included in the predetermined list ofactions that the road user ends up performing is marked as the correctintent from the object's original position (when the intent would bedetermined). In this regard, the intent of the object need not simply bethe arbitrary action itself (i.e. making a left turn or a right turn),but rather the actual trajectory (for instance, defined by a set oflocations where the object was located at different points in time) thatthe object follows. This is then may be used as input to train the modelfor predicting intent of an object.

With this intent information, the vehicle's computing devices canmaneuver the vehicle in order to promote the object's intent. Forexample, if a pedestrian is predicted not to cross a roadway in apredetermined period of time, but the intent prediction is that thepedestrian would like to cross the roadway, the computing devices of thevehicle may control the vehicle to allow the pedestrian to cross theroadway. Similar maneuvering can be used to promote the intent of abicyclist or vehicle. Reasoning about the intents of objects, such asother road users, is powerful and provides an additional level ofsophistication, concern for others and robustness to the vehicle. Italso allows the autonomous vehicle function in a more “human-like” or“polite” way.

Example Systems

As shown in FIG. 1, a vehicle 100 in accordance with one aspect of thedisclosure includes various components. While certain aspects of thedisclosure are particularly useful in connection with specific types ofvehicles, the vehicle may be any type of vehicle including, but notlimited to, cars, trucks, motorcycles, busses, recreational vehicles,etc. The vehicle may have one or more computing devices, such ascomputing device 110 containing one or more processors 120, memory 130and other components typically present in general purpose computingdevices.

The memory 130 stores information accessible by the one or moreprocessors 120, including instructions 132 and data 134 that may beexecuted or otherwise used by the processor 120. The memory 130 may beof any type capable of storing information accessible by the processor,including a computing device-readable medium, or other medium thatstores data that may be read with the aid of an electronic device, suchas a hard-drive, memory card, ROM, RAM, DVD or other optical disks, aswell as other write-capable and read-only memories. Systems and methodsmay include different combinations of the foregoing, whereby differentportions of the instructions and data are stored on different types ofmedia.

The instructions 132 may be any set of instructions to be executeddirectly (such as machine code) or indirectly (such as scripts) by theprocessor. For example, the instructions may be stored as computingdevice code on the computing device-readable medium. In that regard, theterms “instructions” and “programs” may be used interchangeably herein.The instructions may be stored in object code format for directprocessing by the processor, or in any other computing device languageincluding scripts or collections of independent source code modules thatare interpreted on demand or compiled in advance. Functions, methods androutines of the instructions are explained in more detail below.

The data 134 may be retrieved, stored or modified by processor 120 inaccordance with the instructions 132. For instance, although the claimedsubject matter is not limited by any particular data structure, the datamay be stored in computing device registers, in a relational database asa table having a plurality of different fields and records, XMLdocuments or flat files. The data may also be formatted in any computingdevice-readable format.

The one or more processor 120 may be any conventional processors, suchas commercially available CPUs. Alternatively, the one or moreprocessors may be a dedicated device such as an ASIC or otherhardware-based processor. Although FIG. 1 functionally illustrates theprocessor, memory, and other elements of computing device 110 as beingwithin the same block, it will be understood by those of ordinary skillin the art that the processor, computing device, or memory may actuallyinclude multiple processors, computing devices, or memories that may ormay not be stored within the same physical housing. For example, memorymay be a hard drive or other storage media located in a housingdifferent from that of computing device 110. Accordingly, references toa processor or computing device will be understood to include referencesto a collection of processors or computing devices or memories that mayor may not operate in parallel.

Computing device 110 may all of the components normally used inconnection with a computing device such as the processor and memorydescribed above as well as a user input 150 (e.g., a mouse, keyboard,touch screen and/or microphone) and various electronic displays (e.g., amonitor having a screen or any other electrical device that is operableto display information). In this example, the vehicle includes aninternal electronic display 152 as well as one or more speakers 154 toprovide information or audio visual experiences. In this regard,internal electronic display 152 may be located within a cabin of vehicle100 and may be used by computing device 110 to provide information topassengers within the vehicle 100.

Computing device 110 may also include one or more wireless networkconnections 154 to facilitate communication with other computingdevices, such as the client computing devices and server computingdevices described in detail below. The wireless network connections mayinclude short range communication protocols such as Bluetooth, Bluetoothlow energy (LE), cellular connections, as well as various configurationsand protocols including the Internet, World Wide Web, intranets, virtualprivate networks, wide area networks, local networks, private networksusing communication protocols proprietary to one or more companies,Ethernet, WiFi and HTTP, and various combinations of the foregoing.

In one example, computing device 110 may be an autonomous drivingcomputing system incorporated into vehicle 100. The autonomous drivingcomputing system may capable of communicating with various components ofthe vehicle. For example, returning to FIG. 1, computing device 110 maybe in communication with various systems of vehicle 100, such asdeceleration system 160, acceleration system 162, steering system 164,signaling system 166, navigation system 168, positioning system 170, anddetection system 172 in order to control the movement, speed, etc. ofvehicle 100 in accordance with the instructions 134 of memory 130.Again, although these systems are shown as external to computing device110, in actuality, these systems may also be incorporated into computingdevice 110, again as an autonomous driving computing system forcontrolling vehicle 100.

As an example, computing device 110 may interact with decelerationsystem 160 and acceleration system 162 in order to control the speed ofthe vehicle. Similarly, steering system 164 may be used by computer 110in order to control the direction of vehicle 100. For example, ifvehicle 100 is configured for use on a road, such as a car or truck, thesteering system may include components to control the angle of wheels toturn the vehicle. Signaling system 166 may be used by computing device110 in order to signal the vehicle's intent to other drivers orvehicles, for example, by lighting turn signals or brake lights whenneeded.

Navigation system 168 may be used by computing device 110 in order todetermine and follow a route to a location. In this regard, thenavigation system 168 and/or data 134 may store detailed mapinformation, e.g., highly detailed maps identifying the shape andelevation of roadways, lane lines, intersections, crosswalks, speedlimits, traffic signals, buildings, signs, real time trafficinformation, vegetation, or other such objects and information.

FIG. 2 is an example of map information 200 for a section of roadwayincluding intersections 202 and 204. In this example, the detailed mapinformation 200 includes information identifying the shape, location,and other characteristics of lane lines 210, 212, 214, traffic signallights 220, 222, crosswalks 230, 232, sidewalks 240, stop signs 250,252, and yield sign 260. Areas where the vehicle can drive may beassociated with one or more rails 270, 272, and 274 which indicate thelocation and direction in which a vehicle should generally travel atvarious locations in the map information. For example, a vehicle mayfollow rail 270 when driving in the lane between lane lines 210 and 212,and may transition to rail 272 in order to make a right turn atintersection 204. Thereafter the vehicle may follow rail 274. Of course,given the number and nature of the rails only a few are depicted in mapinformation 200 for simplicity and ease of understanding.

Although the map information is depicted herein as an image-based map,the map information need not be entirely image based (for example,raster). For example, the map information may include one or moreroadgraphs or graph networks of information such as roads, lanes,intersections, and the connections between these features. Each featuremay be stored as graph data and may be associated with information suchas a geographic location and whether or not it is linked to otherrelated features, for example, a stop sign may be linked to a road andan intersection, etc. In some examples, the associated data may includegrid-based indices of a roadgraph to allow for efficient lookup ofcertain roadgraph features.

Positioning system 170 may be used by computing device 110 in order todetermine the vehicle's relative or absolute position on a map or on theearth. For example, the position system 170 may include a GPS receiverto determine the device's latitude, longitude and/or altitude position.Other location systems such as laser-based localization systems,inertial-aided GPS, or camera-based localization may also be used toidentify the location of the vehicle. The location of the vehicle mayinclude an absolute geographical location, such as latitude, longitude,and altitude as well as relative location information, such as locationrelative to other cars immediately around it which can often bedetermined with less noise that absolute geographical location.

The positioning system 170 may also include other devices incommunication with computing device 110, such as an accelerometer,gyroscope or another direction/speed detection device to determine thedirection and speed of the vehicle or changes thereto. By way of exampleonly, an acceleration device may determine its pitch, yaw or roll (orchanges thereto) relative to the direction of gravity or a planeperpendicular thereto. The device may also track increases or decreasesin speed and the direction of such changes. The device's provision oflocation and orientation data as set forth herein may be providedautomatically to the computing device 110, other computing devices andcombinations of the foregoing.

The perception system 172 also includes one or more components fordetecting objects external to the vehicle such as other vehicles,obstacles in the roadway, traffic signals, signs, trees, etc. Forexample, the detection system 170 may include lasers, sonar, radar,cameras and/or any other detection devices that record data which may beprocessed by computing device 110. In the case where the vehicle is asmall passenger vehicle such as a car, the car may include a laser orother sensors mounted on the roof or other convenient location. Forinstance, a vehicle's perception system may use various sensors, such asLIDAR, sonar, radar, cameras, etc. to detect objects and theircharacteristics such as location, orientation, size, shape, type,direction and speed of movement, etc. The raw data from the sensorsand/or the aforementioned characteristics can be quantified or arrangedinto a descriptive function or vector for processing by the computingdevice 110. As discussed in further detail below, computing device 110may use the positioning system 170 to determine the vehicle's locationand perception system 172 to detect and respond to objects when neededto reach the location safely.

FIGS. 3A-3D are examples of external views of vehicle 100. As can beseen, vehicle 100 includes many features of a typical vehicle such asheadlights 302, windshield 303, taillights/turn signal lights 304, rearwindshield 305, doors 306, side view mirrors 308, tires and wheels 310,and turn signal/parking lights 312. Headlights 302, taillights/turnsignal lights 304, and turn signal/parking lights 312 may be associatedthe signaling system 166. Light bar 307 may also be associated with thesignaling system 166.

Vehicle 100 also includes sensors of the perception system 172. Forexample, housing 314 may include one or more laser devices for having360 degree or narrower fields of view and one or more camera devices.Housings 316 and 318 may include, for example, one or more radar and/orsonar devices. The devices of the detection system may also beincorporated into the typical vehicle components, such as taillights 304and/or side view mirrors 308. Each of these radar, camera, and lasersdevices may be associated with processing components which process datafrom these devices as part of the detection system 172 and providesensor data to the computing device 110.

Data 134 may store various behavior-time models for predicting anobjects future behavior for a predetermined period of time. Forinstance, data from the perception system 172 may be used to both make aprediction about what an object will do in the future. This is typicallydone by reasoning over what another road user will do over some timehorizon. For instance, the vehicle's computing devices may generate aprediction of what a pedestrian will do over the next ten seconds, orover a time period corresponding to how long the autonomous vehicle, mayinteract with the pedestrian (e.g. until the vehicle has passed byand/or the pedestrian is at least some distance, such as 20 meters ormore or less, behind the vehicle). Thus, the typical predeterminedperiod may be 10 seconds or more or less, though shorter periods may beused where the object is likely to be passed by the vehicle (such thatthe object becomes no longer relevant to controlling the vehicle). Thispredetermined period of time may also be stored in data 134. Of course,the farther into the future the prediction is made, or rather the longerthe predetermined period of time, the less reliable the predictionbecomes.

Data 134 may store various behavior-time models for predicting anobjects future behavior for a per-determined period of time. In oneexample, the behavior-time models may be configured to use data for anobject received from the perception system 172, and in particularanother road user, including the road user's characteristics as well asadditional contextual information discussed in further detail below. Asan example, given the location, heading, speed, and othercharacteristics included in the data from the perception system 172, thebehavior-time models may provide a set of one or more predictions forhow the object could behave for the predetermined period of time as wellas a corresponding likelihood value for each prediction. The likelihoodvalues may indicate which of the predictions are more likely to occur(relative to one another). In this regard, the prediction with thegreatest likelihood value may be the most likely to occur whereaspredictions with lower likelihood values may be less likely to occur.

The behavior-time models may be configured to generate a set of possiblehypotheses for what a particular road user will do over a particularhorizon or predetermined period of time (e.g. 10 seconds) and relativelikelihoods for each hypothesis. These models may be trained using dataabout how an object observed at that location behaved in the past,intuition, etc. The computing device 110 can then reason abouthypotheses that interact with the vehicle's trajectory and are of asufficient likelihood to be worth considering.

In addition to predicting what an object will do for a predeterminedperiod into the future, the computing devices 110 may also use data fromthe perception system to “reason” about another road user's intent. Inthis regard, data 134 may store intent models configured to use the rawdata from the sensors and/or the characteristics described above incombination with contextual information to predict the next action ofthe predetermined list of actions that the road user is expected to takeand a given point in time when that action is likely to be initiated bythe other road user. Again, the predicted next action can include theaction itself, but also a predicted trajectory (for instance, defining aset of future locations where the other road user will be at varioustimes in the future) for that action.

As with the behavior-time models, the intent models may provide a set ofpossible intents or hypotheses identifying possible next actions (andpredicted trajectories), a given point in time when the action is likelyto occur, and associated likelihood values. As an example, given thelocation, heading, speed, and other characteristics included in the datafrom the perception system 172, the intent models may provide a set ofone or more predictions for possible next actions. The likelihood valuesmay indicate which of the predictions are more likely to occur (relativeto one another). In this regard, the prediction with the greatestlikelihood value may be the most likely to occur whereas predictionswith lower likelihood values may be less likely to occur.

In order to provide these intents, data 134 may store the predeterminedlist of actions. This list may be generated manually, for instance, byhuman operators based on personal experience or observations of theactions of other road users (by such operators or from sensor data fromperception systems of one or more autonomous vehicles such as vehicles100 or 100A). The predetermined list of actions may actually include aplurality of sub-lists each associated with a particular type of roaduser, such as other vehicles, bicyclists, and pedestrians. For instance,for a vehicle, the predetermined list of actions may include following acurrent lane of the vehicle, changing lanes, turning at intersections,making a U-turns, entering or exiting driveways, pulling over, pullingout from a parking spot or parked position. Similarly, for cyclists, thepredetermined list of actions may include all of the actions forvehicles as well as crossing crosswalks and “jaywalking” (for instance,crossing a roadway at a location other than an intersection orcrosswalk). For a pedestrian, the predetermined list of actions mayinclude crossing a roadway, crossing a roadway in a crosswalk,jaywalking, following a curb, sidewalk or edge of a lane at the side ofroad, entering a vehicle, etc. Moreover, the “actions” may be defined bytrajectories corresponding to expected locations of an objectcorresponding to the object performing the action over time.

To train an intent model to generate a set of possible intents for anobject, rather than using a fixed time horizon and observing what theobject does during that fixed time horizon, the training data may lookas far into the future as it takes for the road user to perform anyaction included in the list of predetermined actions. Then, the nextaction or trajectory of the object (for instance, defined by a set oflocations where the object has been located) that corresponds to anaction included in the list of predetermined actions that the road userends up performing is marked as the correct intent from the object'soriginal position (when the intent would be determined).

The computing device 110 may control the direction and speed of thevehicle by controlling various components. By way of example, computingdevice 110 may navigate the vehicle to a destination location completelyautonomously using data from the detailed map information and navigationsystem 168. In order to maneuver the vehicle, computing device 110 maycause the vehicle to accelerate (e.g., by increasing fuel or otherenergy provided to the engine by acceleration system 162), decelerate(e.g., by decreasing the fuel supplied to the engine, changing gears,and/or by applying brakes by deceleration system 160), change direction(e.g., by turning the front or rear wheels of vehicle 100 by steeringsystem 164), and signal such changes (e.g., by lighting turn signals ofsignaling system 166). Thus, the acceleration system 162 anddeceleration system 162 may be a part of a drivetrain that includesvarious components between an engine of the vehicle and the wheels ofthe vehicle. Again, by controlling these systems, computing device 110may also control the drivetrain of the vehicle in order to maneuver thevehicle autonomously.

The one or more computing devices 110 of vehicle 100 may also receive ortransfer information to and from other computing devices. FIGS. 4 and 5are pictorial and functional diagrams, respectively, of an examplesystem 400 that includes a plurality of computing devices 410, 420, 430,440 and a storage system 450 connected via a network 440. System 400also includes vehicle 100, and vehicle 100A which may be configuredsimilarly to vehicle 100. Although only a few vehicles and computingdevices are depicted for simplicity, a typical system may includesignificantly more.

As shown in FIG. 4, each of computing devices 410, 420, 430, 440 mayinclude one or more processors, memory, data and instructions. Suchprocessors, memories, data and instructions may be configured similarlyto one or more processors 120, memory 130, data 132, and instructions134 of computing device 110.

The network 440, and intervening nodes, may include variousconfigurations and protocols including short range communicationprotocols such as Bluetooth, Bluetooth LE, the Internet, World Wide Web,intranets, virtual private networks, wide area networks, local networks,private networks using communication protocols proprietary to one ormore companies, Ethernet, WiFi and HTTP, and various combinations of theforegoing. Such communication may be facilitated by any device capableof transmitting data to and from other computing devices, such as modemsand wireless interfaces.

In one example, one or more computing devices 410 may include a serverhaving a plurality of computing devices, e.g., a load balanced serverfarm, that exchange information with different nodes of a network forthe purpose of receiving, processing and transmitting the data to andfrom other computing devices. For instance, one or more computingdevices 210 may include one or more server computing devices that arecapable of communicating with one or more computing devices 110 ofvehicle 100 or a similar computing device of vehicle 100A as well asclient computing devices 420, 430, 440 via the network 440. For example,vehicles 100 and 100A may be a part of a fleet of vehicles that can bedispatched by server computing devices to various locations. In thisregard, the vehicles of the fleet may periodically send the servercomputing devices location information provided by the vehicle'srespective positioning systems and the one or more server computingdevices may track the locations of the vehicles.

In addition, server computing devices 410 may use network 440 totransmit and present information to a user, such as user 422, 432, 442on a display, such as displays 424, 434, 444 of computing devices 420,430, 440. In this regard, computing devices 420, 430, 440 may beconsidered client computing devices.

As shown in FIG. 5, each client computing device 420, 430, 440 may be apersonal computing device intended for use by a user 422, 432, 442, andhave all of the components normally used in connection with a personalcomputing device including a one or more processors (e.g., a centralprocessing unit (CPU)), memory (e.g., RAM and internal hard drives)storing data and instructions, a display such as displays 424, 434, 444(e.g., a monitor having a screen, a touch-screen, a projector, atelevision, or other device that is operable to display information),and user input devices 426, 436, 446 (e.g., a mouse, keyboard,touch-screen or microphone). The client computing devices may alsoinclude a camera for recording video streams, speakers, a networkinterface device, and all of the components used for connecting theseelements to one another.

Although the client computing devices 420, 430, and 440 may eachcomprise a full-sized personal computing device, they may alternativelycomprise mobile computing devices capable of wirelessly exchanging datawith a server over a network such as the Internet. By way of exampleonly, client computing device 420 may be a mobile phone or a device suchas a wireless-enabled PDA, a tablet PC, a wearable computing device orsystem, laptop, or a netbook that is capable of obtaining informationvia the Internet or other networks. In another example, client computingdevice 430 may be a wearable computing device, such as a “smart watch”as shown in FIG. 4. As an example the user may input information using akeyboard, a keypad, a multi-function input button, a microphone, visualsignals (for instance, hand or other gestures) with a camera or othersensors, a touch screen, etc.

In some examples, client computing device 440 may be concierge workstation used by an administrator to provide concierge services to userssuch as users 422 and 432. For example, a concierge 442 may use theconcierge work station 440 to communicate via a telephone call or audioconnection with users through their respective client computing devicesor vehicles 100 or 100A in order to ensure the safe operation ofvehicles 100 and 100A and the safety of the users as described infurther detail below. Although only a single concierge work station 440is shown in FIGS. 4 and 5, any number of such work stations may beincluded in a typical system.

Storage system 450 may store various types of information as describedin more detail below. This information may be retrieved or otherwiseaccessed by a server computing device, such as one or more servercomputing devices 410, in order to perform some or all of the featuresdescribed herein. For example, the information may include user accountinformation such as credentials (e.g., a user name and password as inthe case of a traditional single-factor authentication as well as othertypes of credentials typically used in multi-factor authentications suchas random identifiers, biometrics, etc.) that can be used to identify auser to the one or more server computing devices. The user accountinformation may also include personal information such as the user'sname, contact information, identifying information of the user's clientcomputing device (or devices if multiple devices are used with the sameuser account), as well as one or more unique signals for the user.

The storage system 450 may also store routing data for generating andevaluating routes between locations. For example, the routinginformation may be used to estimate how long it would take a vehicle ata first location to reach a second location. In this regard, the routinginformation may include map information, not necessarily as particularas the detailed map information described above, but including roads, aswell as information about those road such as direction (one way, twoway, etc.), orientation (North, South, etc.), speed limits, as well astraffic information identifying expected traffic conditions, etc.

As with memory 130, storage system 250 can be of any type ofcomputerized storage capable of storing information accessible by theserver computing devices 410, such as a hard-drive, memory card, ROM,RAM, DVD, CD-ROM, write-capable, and read-only memories. In addition,storage system 450 may include a distributed storage system where datais stored on a plurality of different storage devices which may bephysically located at the same or different geographic locations.Storage system 450 may be connected to the computing devices via thenetwork 440 as shown in FIG. 4 and/or may be directly connected to orincorporated into any of the computing devices 110, 410, 420, 430, 440,etc.

Example Methods

In addition to the operations described above and illustrated in thefigures, various operations will now be described. It should beunderstood that the following operations do not have to be performed inthe precise order described below. Rather, various steps can be handledin a different order or simultaneously, and steps may also be added oromitted.

In one aspect, a user may download an application for requesting avehicle to a client computing device. For example, users 422 and 432 maydownload the application via a link in an email, directly from awebsite, or an application store to client computing devices 420 and430. For example, client computing device may transmit a request for theapplication over the network, for example, to one or more servercomputing devices 410, and in response, receive the application. Theapplication may be installed locally at the client computing device.

The user may then use his or her client computing device to access theapplication and request a vehicle. As an example, a user such as user432 may use client computing device 430 to send a request to one or moreserver computing devices 410 for a vehicle. The request may includeinformation identifying a pickup location or area and/or a destinationlocation or area. In response the one or more server computing devices410 may identify and dispatch, for example based on availability andlocation, a vehicle to the pickup location. This dispatching may involvesending information to the vehicle identifying the user (and/or theuser's client device) in order to assign the vehicle to the user (and/orthe user's client computing device), the pickup location, and thedestination location or area.

Once the vehicle 100 receives the information dispatching the vehicle,the vehicle's one or more computing devices 110 may maneuver the vehicleto the pickup location using the various features described above. Oncethe user, now passenger, is safely in the vehicle, the computer 110 mayinitiate the necessary systems to control the vehicle autonomously alonga route to the destination location. For instance, the navigation system168 may use the map information of data 134 to determine a route or pathto the destination location that follows a set of connected rails of mapinformation 200. The computing devices 110 may then maneuver the vehicleautonomously (or in an autonomous driving mode) as described above alongthe route towards the destination. FIGS. 6-9 are example views ofvehicle 100 driving on a roadway 600 corresponding to the detailed mapinformation 200.

Each of the examples depicts a section of roadway 600 includingintersections 602 and 604. In this example, intersections 602, 604, and606 corresponds to intersections 202 and 204 of the map information 200,respectively. In this example, lane lines 610, 612, and 614 correspondto the shape, location, and other characteristics of lane lines 210,212, and 214, respectively. Similarly, crosswalks 630 and 632 correspondto the shape, location, and other characteristics of crosswalks 230 and232, respectively; sidewalks 640 correspond to sidewalks 240; trafficsignal lights 622, 624, and 626 correspond to traffic signal lights 222,224 and 226, respectively; stop signs 650, 652 correspond to stop signs250, 252, respectively; and yield sign 660 corresponds to yield sign260. In each example, vehicle 100 is following a route (indicated bydashed lines 670, 770, 870, 970, 1070) to a destination (not shown).

As noted above, as the vehicle is being maneuvered by the computingdevice 110, the perception system 172 may provide the computing deviceswith information about objects, including other road users, detected inthe vehicle's environment. For instance, FIG. 6 depicts vehicle 100approaching intersection 604 in order to make a left turn. Theperception system 172 may provide the computing device 110 withinformation about pedestrian 660 such as its location, heading,velocity, etc. FIG. 7 depicts vehicle 100 approaching intersection 604and moving towards intersection 602. The perception system may providethe computing device with information about vehicle 720 as well aspedestrian 730 standing behind the vehicle 720. FIG. 8 depicts vehicle100 approaching intersection 604 and moving towards intersection 602.The perception system may provide the computing device with informationabout vehicle 820 as well as pedestrian 830 walking across a first lane810 of the roadway between intersection 602 and 604. FIG. 9 depictsvehicle 100 approaching intersection 604 and moving towards intersection602. The perception system may provide the computing device withinformation about vehicle 920 located proximate to an edge 922 of theroadway at a parking lot 924 between intersections 602 and 604. In thisexample, parking lot 920 may also be defined in the detailed mapinformation 200.

The raw data and/or characteristics of another road user received fromthe perception system may be used with contextual information as inputto a behavior-time model of data 134 to make a prediction about whatother road users are going to do for the predetermined period of time.For instance, information such as the road user's type, location, recentmotion heading, acceleration, and velocity may be combined with otherinformation such as where the pedestrian is in the world using thedetailed map information discussed above (e.g. proximity to anintersection, crosswalk, or crosswalk buttons) and used as input to abehavior-time model. The contextual information may also include thestatus of other objects in the environment such as the states of trafficlights, features of other objects (such as vehicles) that might becrossing the pedestrian's path or a crosswalk near the pedestrian mayalso be used as input to the model. In addition, specific details aboutthe road user including gaze detection (i.e. where does the road userappear to be looking, if applicable), proximity to crosswalk buttons,whether the road user is attempting to push a crosswalk button, pointingin a particular direction, or standing in road, etc., may also becontextual information and used as input to the model.

As noted above, the behavior-time model may provide a set of hypotheseseach having an associated likelihood value. As an example, one or moreof those hypotheses with the highest likelihoods (or those above aparticular threshold such that a given hypothesis is considered to havea reasonable likelihood of occurrence) may be identified as an actualfuture trajectory or behavior prediction for the object over thepredetermined period of time. For instance, if the road user is apedestrian wanted to cross a roadway, the prediction for thepredetermined period of time may include details about where thepedestrian is expected to be during the course of the predeterminedperiod of time. As an example, this may include a period during whichthe vehicle is expected to pass the pedestrian according to thevehicle's current or expected speed, acceleration, and heading.

For instance, the vehicle's computing devices may use a behavior-timemodel to generate a prediction of what a road user will do during thenext predetermined period of time (e.g. 10 seconds) or a time periodcorresponding to how long the vehicle 100 is likely to interact with theroad user (e.g. until the road user is well behind the autonomousvehicle). Returning to FIG. 6, the computing device may predict thatpedestrian 660 is going to remain stationary or wait proximate to thecrosswalk 630 without entering the crosswalk for the next 10 seconds (oruntil the vehicle 100 passes by the pedestrian 660). In other words, thelocation and velocity of the pedestrian 660 in combination with thelocation of the vehicle 100 as well as other contextual information maymake it most likely that the pedestrian will not cross the crosswalk 630until after vehicle 100 has passed by the pedestrian.

Regarding FIG. 7, the computing device may use a behavior-time model topredict that pedestrian 730 will remain behind the vehicle 720 for thenext 10 seconds (or until the vehicle 100 passes by the pedestrian 730.Thus, in this example, the location and velocity of the pedestrian incombination with the location of the vehicle 100 as well as othercontextual information may make it most likely that the pedestrian willremain behind the vehicle 730 until after vehicle 100 has passed by thepedestrian.

For FIG. 8, the computing device may use a behavior-time model topredict that pedestrian 730 will remain in lane 830 for the next 10seconds (or until the vehicle 100 passes by the pedestrian 730). Thus,in this example, the location and velocity of the pedestrian incombination with the location of the vehicle 100 as well as othercontextual information may make it most likely that the pedestrian willremain in the lane 810 until after vehicle 100 has passed by thepedestrian.

Regarding FIG. 9, the computing device may use a behavior-time model topredict that the vehicle 920 will remain stationary proximate to edge922 for the next 10 seconds (or until the vehicle 100 passes by thevehicle 920). Thus, in this example, the location and velocity of thepedestrian in combination with the location of the vehicle 100 as wellas other contextual information may make it most likely that thepedestrian will remain proximate to edge 922 until after vehicle 100 haspassed by the pedestrian.

In addition to making a prediction for the predetermined period of time,the raw data and/or characteristics of another road user received fromthe perception system as well as additional contextual information maybe used as input to an intent model of data 134 to make a predictionabout whether the road user will eventually take any of the actions ofthe predetermined list of actions. In other words, without using anyparticular limitation on the timing of the action, the computing device110 may use an intent model of data 134 to estimate when the other roaduser is likely to initiate and/or complete one of the actions of thepredetermined list of action of data 134. As with the behavior-timemodel, the intent model may provide a set of intents or hypothesesidentifying a possible next action (or predicted trajectorycorresponding to an action) included in the predetermined list ofactions, a given point in time when the action is likely to occur, andassociated likelihood values. As an example, one or more of thosehypotheses with the highest likelihoods (or those above a particularthreshold) may be identified as a predicted intent including the nextaction and predicted trajectory for the road user.

For instance, turning to FIG. 6, given the location and velocity of thepedestrian 660 in combination with the location of the vehicle 100 aswell as other contextual information may make it most likely that thepedestrian will cross the crosswalk 630 until after vehicle 100 haspassed by the pedestrian. Thus, the predicted next action (and predictedtrajectory) for pedestrian 660 may be to cross crosswalk 630. In FIG. 7,the location and velocity of the pedestrian 730 in combination with thelocation of the vehicle 100 as well as other contextual information maymake it most likely that the pedestrian will enter vehicle 720 aftervehicle 100 has passed by the pedestrian. Thus, the predicted nextaction (and predicted trajectory) for pedestrian 730 may be to entervehicle 720. In FIG. 8, the location and velocity of the pedestrian 830in combination with the location of the vehicle 100 as well as othercontextual information may make it most likely that the pedestrian willcross into lane 812 in order to complete crossing the roadway aftervehicle 100 has passed by the pedestrian. Thus, the predicted nextaction (and predicted trajectory) for pedestrian 830 may be to crosslane 812. In FIG. 9, the location and velocity of the vehicle 920 incombination with the location of the vehicle 100 as well as othercontextual information may make it most likely that the vehicle 920 willcross edge 922 to leave parking lot 924 after vehicle 100 has passed bythe vehicle 920. Thus, the predicted next action (and predictedtrajectory) for vehicle 920 may be to cross edge 922 to leave parkinglot 924.

In many cases, including the examples above, the next actions (andpredicted trajectories) may be predicted to occur after thepredetermined period of time and thus are important cues to the intentof a particular road user. In other words, the computing devices maydetermine that it is not likely (or the likelihood value is relativelylow) that the object will initiate the predicted next action within thepredetermined period of time. In this regard, the predetermined list ofactions may allow the computing devices to predict another road user'sintent even where the road user will not act on that intent during thepredetermined period of time (i.e. not until the road user has beenpassed by the vehicle 100), for instance because the roadway is notclear or it is otherwise not safe to initiate the action associated withthe intent. Various examples of intent and actions for a pedestrian mayinclude, for instance, a pedestrian waiting to cross the road, theintent is to cross the road when clear (as with the example of FIG. 6);a pedestrian standing at the front or rear of a vehicle, the intent maybe to enter the vehicle when traffic is clear (as with the example ofFIG. 7); and a pedestrian walking behind traffic towards a median, theintent is to cross over the entire road when traffic is clear (as withthe example of FIG. 8). Examples for a vehicle may include, forinstance, a vehicle waiting to pass into oncoming traffic from adriveway or parking lot, the intent is to enter the roadway when clear(as with the example of FIG. 9); a vehicle waiting to turn acrossoncoming traffic into a driveway, the intent is to enter driveway whenclear; a vehicle waiting to turn left at an unprotected intersection,the intent is to turn left when clear; a vehicle waiting its turn at afour way stop and will wait until other higher precedence vehicles havehad their chance to go, the intent is to travel through intersection; avehicle waiting in a driveway, the intent is to pull out into a lane; avehicle waiting at an intersection, the intent is to move through theintersection when clear; a vehicle moving towards another lane, theintent is to change lanes when desired lane is free of traffic; avehicle waiting on the side of a roadway, the intent may be to unparkand pull out onto the roadway. Of course, any of these examples with avehicle may also apply to bicyclists.

With this intent information, the vehicle's computing device 110 canmaneuver the vehicle in order to promote the other road user's intent.In this regard, the computing devices may use the predicted trajectoryof the predicted intent to control the vehicle. For example, if apedestrian is predicted not to cross a roadway in a predetermined periodof time, but the intent prediction is that the pedestrian would like tocross the roadway, the computing devices of the vehicle may control thevehicle to allow the pedestrian to cross the roadway according to thepredicted trajectory. Turning to FIGS. 6-8, this would involve vehicle100 stopping and waiting for pedestrian 660 to cross in crosswalk 630,stopping and waiting for pedestrian 730 to cross the roadway or entervehicle 720, and stopping an waiting for pedestrian 830 to cross lane812. Similar maneuvering can be used to promote the intent of abicyclist or vehicle. For instance, in FIG. 9, this would involvevehicle 100 stopping and waiting for vehicle 920 to exit from parkinglot 924 and cross edge 922.

In addition, as noted above, the computing devices may actually use thepredicted trajectories of multiple different predicted intents. This maybe especially useful where many different predicted trajectories forpredicted intents including the same next action have a high or even lowlikelihood value of occurring. For instance, the computing devices mayuse the predicted trajectories of multiple different intents for a roaduser where those intents each have a high (25%-30% likelihood value).This may be likely to occur when there is relative uncertainty aboutwhich action the road user actually intends to take.

However, even where the object will not perform the next action of thepredicted intent within the predetermined period of time, the vehicle'scomputing devices need not always control the vehicle in order topromote the predicted intent of the other road user. In general, in anygiven situation, the computing devices may consider multiple factors todetermine whether it should maneuver the vehicle in such a way as topromote the predicted intent of the road user, and depending on context,may or may not promote the predicted intent. For instance, if promotingthe intent is not actually appropriate for the current situation, thecomputing devices may actually ignore the predicted intent, such as whendoing so would be unsafe for the object and/or the vehicle. As anexample, the computing devices may detect a pedestrian that intends tocross the road not in a crosswalk. If the computing devices would haveto brake very hard in order to abruptly decelerate the vehicle, it mayactually be safer not to stop and wait for the pedestrian to cross butrather to proceed passed the pedestrian without stopping or yielding(slowing down if needed).

Similar actions may be taken where the vehicle may actually haveprecedence (or the legal right of way according to traffic laws includedin the map information) relative to the other road user. For instance,in some situations, the vehicle may have precedence to another vehiclethat is unparking, exiting a driveway, or turning at an intersection. Inthese cases, the computing devise may actually act on the precedence,rather than slowing down or stopping to let the other road user withlower precedence act on the predicted intent for the road user.

In addition, in cases where the vehicle has lower precedence, thecomputing devices may be more inclined to react to the intent of otherroad users with higher precedence. In an example scenario, the computingdevices may be determining whether the vehicle can pass around anotherroad user that is stopped in traffic ahead of the vehicle. Morespecifically, the computing devices may be deciding whether or not topass to the right of a stopped vehicle ahead of the vehicle. Theprediction of intent may include that the stopped vehicle intends toproceed straight at an upcoming intersection, while the vehicle needs toturn right at that same intersection. If there is enough room to theright of the stopped vehicle, in such scenarios, the computing devicesmay consider the prediction of the stopped vehicle's intent, becausethat stopped vehicle has higher precedence over the vehicle (i.e. thestopped vehicle is directly in front of the vehicle). If the stoppedvehicle also intends to turn right, the computing devices may decide tostay behind the stopped vehicle to let that stopped vehicle make theright turn. Alternatively, if the prediction intent includes a very highlikelihood that the stopped vehicle is not intending to turn rightright, and there's sufficient room (i.e. more than some multiple greaterthan the width of the vehicle) to the right of the stopped vehicle, thecomputing devices may control the vehicle to go ahead and pass aroundthe stopped vehicle to the right of the stopped vehicle in order to turnright at the intersection.

FIG. 10 is an example flow diagram 1000 in accordance which may beperformed by one or more computing devices of a vehicle, such ascomputing device 110 of vehicle 100. In this example, data is receivedfrom a perception system of the vehicle identifying an objectcorresponding to a road user. The information also identifiescharacteristics of the road user as well as contextual information aboutan environment in which the vehicle is currently driving at block 1010.A prediction of the intent of the object is generated using the data atblock 1020. The prediction of the intent of the object includes a nextaction (and predicted trajectory) to be initiated by the road user and apoint in time for initiation of the next action. In addition, the nextaction (or predicted trajectory corresponding to an action) is includedin a predetermined list of actions. A prediction of the behavior of theroad user for a predetermined period of time into the future isgenerated using the data at block 1030. The prediction of the behavior alikelihood that the road user will initiate the next action (andpredicted trajectory) during the predetermined period of time. Thepredetermined period of time includes at least a period of time duringwhich the vehicle is expected to have passed the object. At bock 1040,when the prediction of the behavior indicates that the road user is notlikely to initiate the next action (and predicted trajectory) during thepredetermined period of time, the vehicle is maneuvered according to theprediction of the intent prior to the vehicle passing the object.

Unless otherwise stated, the foregoing alternative examples are notmutually exclusive, but may be implemented in various combinations toachieve unique advantages. As these and other variations andcombinations of the features discussed above can be utilized withoutdeparting from the subject matter defined by the claims, the foregoingdescription of the embodiments should be taken by way of illustrationrather than by way of limitation of the subject matter defined by theclaims. In addition, the provision of the examples described herein, aswell as clauses phrased as “such as,” “including” and the like, shouldnot be interpreted as limiting the subject matter of the claims to thespecific examples; rather, the examples are intended to illustrate onlyone of many possible embodiments. Further, the same reference numbers indifferent drawings can identify the same or similar elements.

The invention claimed is:
 1. A method of maneuvering a vehicle, themethod comprising: receiving, by one or more processors of the vehicle,data from a perception system of the vehicle identifying a road user,the data also identifying characteristics of the road user as well ascontextual information about an environment in which the vehicle iscurrently driving; generating, by the one or more processors, aprediction of an intent of the road user using the data, wherein theprediction of the intent of the road user includes that the road userintends to perform an action on the roadway on which the vehicle iscurrently driving and a point in time for the road user performing theaction; generating, by the one or more processors, a prediction of abehavior of the road user for a predetermined period of time into thefuture using the data, the prediction of the behavior indicating theroad user is unlikely to perform the action until after a future timethat the vehicle is expected to have passed the road user; andmaneuvering, by the one or more processors, the vehicle in order topromote the prediction of the intent over the prediction of thebehavior.
 2. The method of claim 1, wherein the action is changing laneson the roadway.
 3. The method of claim 1, wherein the action is making au-turn on the roadway.
 4. The method of claim 1, wherein the action isexiting a driveway onto the roadway.
 5. The method of claim 1, whereinthe action is pulling out of a parked position onto the roadway.
 6. Themethod of claim 1, wherein the action is turning at an intersection. 7.The method of claim 1, wherein the road user is a second vehicle.
 8. Themethod of claim 1, wherein generating the prediction of the behaviorincludes using a behavior-time model and generating the prediction ofthe intent includes using an intent model.
 9. The method of claim 8,wherein the behavior-time model is configured to predict what the roaduser will do over a particular time horizon, and the intent model isconfigured to predict a next action using a predetermined list ofactions by looking as far into the future as it takes for the road userto perform the next action.
 10. The method of claim 9, furthercomprising: determining a type of the road user; and selecting thepredetermined list of actions from a plurality of predetermined lists ofactions based on the type of the road user.
 11. The method of claim 1,further comprising, determining whether the point in time is beyond thefuture time that the vehicle is expected to have passed the road user,and wherein maneuvering the vehicle is further based on the determining.12. The method of claim 1, wherein the action is defined by a trajectorycorresponding to expected locations of the road user corresponding tothe road user performing the action over a period of time.
 13. Themethod of claim 1, further comprising, before maneuvering the vehicle inorder to promote the prediction of the intent over the prediction of thebehavior, determining that promoting the prediction of intent isappropriate for a current situation.
 14. The method of claim 13, whereindetermining that promoting the prediction of intent is appropriate for acurrent situation is based on how abruptly brakes of the vehicle wouldneed to be applied in order to promote the prediction of intent.
 15. Themethod of claim 1, further comprising, before maneuvering the vehicle inorder to promote the prediction of the intent over the prediction of thebehavior, determining where the vehicle has precedence with respect tothe road user.
 16. The method of claim 1, wherein the road user is apedestrian.
 17. The method of claim 1, wherein the road user is abicyclist.
 18. A system for maneuvering a vehicle, the system comprisingone or more processors of the vehicle configured to: receive data from aperception system of the vehicle identifying a road user, the data alsoidentifying characteristics of the road user as well as contextualinformation about an environment in which the vehicle is currentlydriving; generate a prediction of an intent of the road user using thedata, wherein the prediction of the intent of the road user includesthat the road user intends to perform an action on the roadway on whichthe vehicle is currently driving and a point in time for the road userperforming the action; generate a prediction of a behavior of the roaduser for a predetermined period of time into the future using the data,the prediction of the behavior indicating the road user is unlikely toperform the action until after a future time that the vehicle isexpected to have passed the road user; and maneuver the vehicle in orderto promote the prediction of the intent over the prediction of thebehavior.
 19. The method of claim 1, wherein the action is one ofchanging lanes on the roadway, making a u-turn on the roadway, exiting adriveway onto the roadway, pulling out of a parked position onto theroadway, or turning at an intersection.
 20. The system of claim 18,further comprising the vehicle.